Spatial Analyses of wild boars data regarding on psycological patterns

Semester Project for Patterns & Trends in Environmental Data FS22

Abstract

The threat of wild boar in cultural sites are becoming a greater problem in recent years. The understanding of wild boar activity patterns is therefore of use. Using the data of wild boar movement from 15.12.2014 until the 15.01.2015 has been evaluated in the following manner: Different environments like (settlements, streets and different land use categories) have been contextualized and rankings have been derived answering questions like “What does a wild boar fear?” and “What are the behavioral constraints of wild boars?”. Further we answered questions like what is the most active, laziest and the most adventurous wild boar. We further derived semi scientific psychological patterns on the individual level to conduct rankings based on the answered questions.

Introduction

In recent decades, an expansion and increase of wild boar (Sus scrofa) populations has been observed across Europe, with colonising agricultural and urban areas, thus increasing problems of cohabitation with humans (Rutten et al., 2020). In particular, the wild boar often comes into conflict with humans, the main problems being the transmission of diseases to domestic animals and humans, collisions with vehicles, disturbance or threat to citizens and damage to gardens, public parks, pasture and agriculture (Meng et al. 2009).

Wild boar damage to croplands is expressed in different ways: direct consumption of crops, rooting in search of bulbs, invertebrates or tubers, seed removal, trampling and damage to agricultural infrastructure (Barrios-García & Ballari 2012). Therefore, it is important to know their activity patterns and where and when the wild boars are in order to take countermeasures.

The research question asked is: How can the different environments (settlements, streets and different land use categories) being contextualized with the activity-patterns of the wild boars. How can this be conceptualized, modeled and further implemented in suitable visualizations to show behavioral patterns given to a certain environmental context and help to answer questions like “What does wild boar fear?” or “What are the behavioral constraints of wild boars?”. Further we evaluated questions like what is the most active, laziest and the most adventurous wild boar. Finally we derived semi scientific psychological patterns on the individual level to conduct rankings based on the answered questions.

Material and Methods

Data Aquision and Preprosessing

For this project we needed the following data: wild boar data are given by the course administration of Patterns & Trends in Environmental Data, ZHAW, orthoimages & maps are given by swisstopo (open access) and the spatial data are given by geovite.ethz.ch (open access). An overview over the used data is given in table 1.

library(knitr)

data_aquisistion <- data.frame(
  Data = c("Wild-Boar", "Orthophoto", "Map", "TLM_Landcover", "TLM_Streets", "TLM_Railway", "TLM_Building", "TLM_LeisureAreas"),
  Data_Format = c(".csv", ".tif", ".tif", ".geojson", ".geojson", ".geojson", ".geojson", ".geojson"),
  Geometry_Type = c("Points", "Raster", "Raster", "Polygon", "Lines", "Lines", "Polygon", "Polygon"),
  Projected_CRS = c("CH1903+ / LV95", "CH1903 / LV03", "CH1903 / LV03", "WGS 84", "CH1903 / LV03", "CH1903 / LV03", "CH1903 / LV03", "CH1903 / LV03")
)

kable(data_aquisistion, col.names = gsub("[_]", " ", names(data_aquisistion)), caption = "Table 1: Overwiev of the used data for this project")

Table 1: Overwiev of the used data for this project

Data	Data Format	Geometry Type	Projected CRS
Wild-Boar	.csv	Points	CH1903+ / LV95
Orthophoto	.tif	Raster	CH1903 / LV03
Map	.tif	Raster	CH1903 / LV03
TLM_Landcover	.geojson	Polygon	WGS 84
TLM_Streets	.geojson	Lines	CH1903 / LV03
TLM_Railway	.geojson	Lines	CH1903 / LV03
TLM_Building	.geojson	Polygon	CH1903 / LV03
TLM_LeisureAreas	.geojson	Polygon	CH1903 / LV03

After loading the necessary libraries and the data acquisition in R (version 4.2.1), we converted their different coordinate reference system into a consistent one (CH1903+ / LV95). Then, the data are clipped to the study area with the extension of 2’567000/1’202000 to 2’573000/1’207000 with the function st_intersection(). The study area is located in the canton of Bern, district Seeland, Commune Gempelen and is located near the lake Neuchâtel. To reduce the amount of data volume - due to hardware limitation - only 7 of 19 wild boars were taken and the time period from 15.12.2014 until the 15.01.2015 was chosen. The result therefore is rather a simplified way to solve our question but includes all the necessary data to answer our research questions.

### Loading Libraries:

library(devtools)
library(ComputationalMovementAnalysisData)
library(ggplot2)
library(readr)
library(dplyr)
library(terra)
library(sf)
library(tmap)
library(SimilarityMeasures)
library(lubridate)
library(tidyr)
library(knitr)


### Data Import:

Wildschwein_sf <- st_as_sf(wildschwein_BE,                            # Convert wild-boar csv-data in Spatial Object
                              coords = c("E", "N"), 
                              crs = 2056)
Study_Area <- st_read("StudyArea.geojson")                            # Import Study Area
Orthophoto <- rast("Swissimage_1m_2014.tif")                          # Import Orthophoto as Raster-Data
Map <- terra::rast("Map_1to25000_2018.tif")                                  # Import Map as Raster-Data
Feldfruechte <- st_read("Feldfruechte_1m_2021.geojson")
TLM_Bodenbedeckung <- st_read("TLM_Bodenbedeckung_2020.geojson")      # Import Landcover-Types
TLM_Strassen <- st_read("TLM_Strassen_2020.geojson")                  # Import Roads
TLM_Eisenbahn <- st_read("TLM_Eisenbahn_2020.geojson")                # Import Railway-Lines
TLM_Gebaeude <- st_read("TLM_Gebaeude_Foodprint_2020.geojson")        # Import Buildings
TLM_Freizeitareale <- st_read("TLM_Freizeitareale_2020.geojson")      # Import Leisure-Areas


### Convert CRS:

Wildschwein_sf          # Projected CRS: CH1903+ / LV95
Study_Area              # Projected CRS: CH1903+ / LV95
Orthophoto              # Projected CRS: CH1903 / LV03
Map                     # Projected CRS: CH1903 / LV03
Feldfruechte            # Projected CRS: CH1903+ / LV95
TLM_Bodenbedeckung      # Projected CRS: WGS 84
TLM_Strassen            # Projected CRS: CH1903 / LV03
TLM_Eisenbahn           # Projected CRS: CH1903 / LV03
TLM_Gebaeude            # Projected CRS: CH1903 / LV03
TLM_Freizeitareale      # Projected CRS: CH1903 / LV03

TLM_Bodenbedeckung <- st_transform(TLM_Bodenbedeckung, crs = st_crs(2056))
TLM_Strassen <- st_transform(TLM_Strassen, crs = st_crs(2056))
TLM_Eisenbahn <- st_transform(TLM_Eisenbahn, crs = st_crs(2056))
TLM_Gebaeude <- st_transform(TLM_Gebaeude, crs = st_crs(2056))
TLM_Freizeitareale <- st_transform(TLM_Freizeitareale, crs = st_crs(2056))
Study_Area_for_Raster <- st_transform(Study_Area, crs = st_crs(21781))


### Objektart as Numeric:

TLM_Bodenbedeckung$objektart <- as.numeric(TLM_Bodenbedeckung$objektart)
TLM_Strassen$objektart <- as.numeric(TLM_Strassen$objektart)
TLM_Eisenbahn$objektart <- as.numeric(TLM_Eisenbahn$objektart)
TLM_Gebaeude$objektart <- as.numeric(TLM_Gebaeude$objektart)
TLM_Freizeitareale$objektart <- as.numeric(TLM_Freizeitareale$objektart)


### Downsizing Orthophoto

Orthophoto <- aggregate(Orthophoto, fact=2)


### Clip Data to Study_Area

Wildschwein_sf <- st_intersection(Wildschwein_sf, Study_Area)
Orthophoto <- crop(x = Orthophoto, y = Study_Area_for_Raster)
Feldfruechte <- st_intersection(Feldfruechte, Study_Area)
TLM_Bodenbedeckung <- st_intersection(TLM_Bodenbedeckung, Study_Area)
TLM_Strassen <- st_intersection(Study_Area, TLM_Strassen)
TLM_Eisenbahn <- st_intersection(Study_Area, TLM_Eisenbahn)
TLM_Gebaeude <- st_intersection(Study_Area, TLM_Gebaeude)
TLM_Freizeitareale <- st_intersection(Study_Area, TLM_Freizeitareale)


### Choose Wild-Boar Data from 2014 to 2015

Wildschwein_select <- Wildschwein_sf %>% 
  filter(DatetimeUTC >= as.Date("2014-12-15") & DatetimeUTC < as.Date("2015-01-15"))

timespan <- as.numeric(difftime(time1 = "2015-01-15", time2 = "2014-12-15", units = "secs"))


### Add Metadata
### Bodenbedeckung:

objektart <- c(1:15)
objektart_Beschreibung <- c("Fels", "Fels locker", "Felsbloecke", "Felsbloecke locker", "Fliessgewaesser", "Gebueschwald", "Lockergestein", "Lockergestein locker", "Gletscher", "Stehende Gewaesser", "Feuchtgebiet", "Wald", "Wald offen", "Gehoelzflaeche", "Schneefeld Toteis")
Attributnamen <- data.frame(objektart, objektart_Beschreibung)

TLM_Bodenbedeckung <-left_join(TLM_Bodenbedeckung, Attributnamen, by = "objektart")


### Strassen:

objektart <- c(1:23)
objektart_Beschreibung <- c("Ausfahrt", "Einfahrt", "Autobahn", "Raststaette", "Verbindung", "Zufahrt", "Dienstzufahrt", "10m Strasse", "6m Strasse", "4m Strasse", "3m Strasse", "Platz", "Autozug", "Faehre", "2m Weg", "1m Weg", "1m Wegfragment", "2m Wegfragment", "Markierte Spur","8m Strasse","Autostrasse","Klettersteig", "Provisorium")
Attributnamen <- data.frame(objektart, objektart_Beschreibung)

TLM_Strassen <-left_join(TLM_Strassen, Attributnamen, by = "objektart")


### Eisenbahn:

objektart <- c(0,2,4,5)
objektart_Beschreibung <- c("Normalspur", "Schmalspur", "Schmalspur mit Normalspur", "Kleinbahn")
Attributnamen <- data.frame(objektart, objektart_Beschreibung)

TLM_Eisenbahn <-left_join(TLM_Eisenbahn, Attributnamen, by = "objektart")


### Gebaeude:

objektart <- c(0:22)
objektart_Beschreibung <- c("Gebaeude", "NA", "Hochhaus", "Hochkamin", "Turm", "Kuehlturm", "Lagertank", "Lueftungsschacht", "Offenes Gebaeude", "Treibhaus", "Im Bau", "Kapelle", "Sakraler Turm", "Sakrales Gebaeude", "NA", "Flugdach", "Unterirdisches Gebaeude", "Mauer gross", "Mauer gross gedeckt", "Historische Baute", "NA", "NA", "Verbindungsbruecke")
Attributnamen <- data.frame(objektart, objektart_Beschreibung)

TLM_Gebaeude <-left_join(TLM_Gebaeude, Attributnamen, by = "objektart")


### Freizeitareale:

objektart <- c(0:7)
objektart_Beschreibung <- c("Campingplatzareal", "Freizeitanlagenareal", "Golfplatzareal", "Pferderennbahnareal", "Schwimmbadareal", "Sportplatzareal", "Standplatzareal", "Zooareal")
Attributnamen <- data.frame(objektart, objektart_Beschreibung)

TLM_Freizeitareale <-left_join(TLM_Freizeitareale, Attributnamen, by = "objektart")

The most active or laziest wild boar

Deriving Speed in order to derive a proxy of the most active or laziest wild boar Deriving another proxy: net displacent In this case to decided to derive the mean speed and mean displacement in one day intervals

1. We roundet the date by days
2. We grouped by TierName and datetime_round
3. We derived the mean speed and displacement

in this case without the geometry because of the reduced computational effort

ws_newyear <- wildschwein_BE %>% 
  filter(DatetimeUTC >= as.Date("2014-12-15") & DatetimeUTC < as.Date("2015-01-15"))

ws_newyear <- ws_newyear %>%
  mutate(timelag = as.numeric(difftime(lead(DatetimeUTC),DatetimeUTC,units = "mins")))


ws_newyear_steplength <- ws_newyear %>%
  group_by(TierName) %>%
  mutate(
    steplength = sqrt((E-lead(E))^2+(N-lead(N))^2)
  )


ws_newyear_speed <- ws_newyear_steplength %>%
  group_by(TierName) %>%
  mutate(
    speed = steplength/timelag
  )

ws_newyear_net_displacement <- ws_newyear %>%
  mutate(
    net_displacement = sqrt((E-lead(E, 20))^2+(N-lead(N, 20))^2)
  )

ws_newyear_speed %>%
  subset(select=-c(E,N)) %>%
  mutate(datetime_round = lubridate::round_date(as.POSIXct(DatetimeUTC),unit = "1 day")) %>%
  group_by(TierName, datetime_round) %>%
  summarise(speed_mean = mean(speed)) -> ws_newyear_speed_mean


ws_newyear_steplength %>%
  subset(select=-c(E,N)) %>%
  mutate(datetime_round = lubridate::round_date(as.POSIXct(DatetimeUTC),unit = "1 day")) %>%
  group_by(TierName, datetime_round) %>%
  summarise(steplength_mean = mean(steplength)) -> ws_newyear_steplength_mean


ws_newyear_net_displacement %>%
  subset(select=-c(E,N)) %>%
  mutate(datetime_round = lubridate::round_date(as.POSIXct(DatetimeUTC),unit = "1 day")) %>%
  group_by(TierName, datetime_round) %>%
  summarise(net_displacement = mean(net_displacement)) -> ws_newyear_net_displacement

Moving window approach

The Moving window approach in order to reduce noise of the data might be another approach to analyse i.e. speed_mean but was not further been elaborated in this project. The idea is to sum up in this case six and five mean variables like speed_mean to smooth out movement peeks over the given timeperiod. This allows to visualize the movement patterns in a more trend driven way.

# Here we can see the overall movements of the seven wild boar in a smooth out way (derived by the rollmean function) and the original speedmean.

ws_newyear_speed_mean %>% 
  head(100) %>%
  mutate(rollmean = zoo::rollmean(speed_mean, 6, fill = NA, align = "center")) %>%
  ggplot(aes(datetime_round)) +
  geom_line(aes(y = speed_mean, colour = "speedmean")) +
  #geom_point(aes(y = speed_mean)) +
  geom_line(aes(y = rollmean, colour = "rollmean")) 

# An idea would have been to derive the rollmean of the individuals and compare them among each other.
# i.e.:
ws_newyear_speed_mean %>% 
  head(37) %>%
  mutate(rollmean = zoo::rollmean(speed_mean, 5, fill = NA, align = "center")) %>%
  ggplot(aes(datetime_round)) +
  geom_line(aes(y = rollmean, colour = "TierName"))

The most fearful and the most brave wild boar

To describe the effect of the human influence areas at the activity-patterns of the wild boars, the question was asked: What is the most fearful and the most brave wild boar? The “fearful” wild boar is defined as the animal which avoids urban areas. The “brave” wild boar is the one that ventures closest to settlements. The ranking was based on the time spent in human influence areas as a percentage over the entire study period (31 days).

To define the human influence areas - such as streets, railway, building and leisure areas - the data sets were first combined by union(). Then a buffer zone of 100 m was created around the human influence areas (buffer()). This range corresponds to the escape distance of 100 m to 200 m of wild boars given in the literature (Suter et al., 2018; Thurfjell et al. 2013). After joining the wild boar data with the Human Influence Areas Buffers (st_join()), the time difference between subsequent rows was calculated with difftime(). All categories of the urban areas were combined and named “Human Influence Areas” and all natural environment as “Nature”. In Figure 1 a) are all human influence areas in the study area shown and in b) the 100 m buffer zone around the human influence areas.

### Combine Human Influence Areas.
### Geometry Type: Lines

Strassen_kl <- TLM_Strassen[,c("objektart_Beschreibung", "geometry")]
Eisenbahn_kl <- TLM_Eisenbahn[,c("objektart_Beschreibung", "geometry")]

Lines <- union(Strassen_kl, Eisenbahn_kl)
Lines <- st_as_sf(Lines)


### Geometry Type: Polygons

Gebaeude_kl <- TLM_Gebaeude[,c("objektart_Beschreibung", "geometry")]
Freizeitareale_kl <- TLM_Freizeitareale[,c("objektart_Beschreibung", "geometry")]

Polygons <- union(Gebaeude_kl, Freizeitareale_kl)
Polygons <- st_as_sf(Polygons)


### Total Human Influence Areas.

Human_Influence_Areas <- union(Lines, Polygons)
Human_Influence_Areas <- st_as_sf(Human_Influence_Areas)


### Create Buffer around Human Influence Areas.

Human_Influence_Areas_Buffer <- st_buffer(x = Human_Influence_Areas, dist = 100)


### Plot Human Influence Areas

Human_Influence_Areas_Plot <- tm_shape(shp = Orthophoto) +
    tm_rgb() +
  tm_shape(shp = Wildschwein_select) + 
    tm_dots(col = "TierName") +
  tm_shape(shp = Human_Influence_Areas) + 
    tm_polygons(col = "objektart_Beschreibung") +
    tm_lines(col = "objektart_Beschreibung") +
  tm_compass(type = "arrow", position = c(0.247,0.028), bg.color = "white", bg.alpha = 0.75, size = 1.8) + 
  tm_scale_bar(breaks = c(0, 0.25, 0.5, 0.75 ,1), text.size = 0.75, position = c("left", "bottom"), bg.color = "white", bg.alpha = 0.75) +
  tm_layout(main.title = "a)",
            main.title.size = 1,
            legend.title.size = 1,
            legend.title.color = "white",
            legend.text.size = 0.6,
            legend.outside = TRUE,
            legend.bg.color = "white",
            legend.bg.alpha = 1)

Human_Influence_Areas_Buffer <- mutate(Human_Influence_Areas_Buffer,Human_Influence_Areas =  "Human Influence Areas")
  
Human_Influence_Areas_Buffer_Plot <- tm_shape(shp = Orthophoto) +
    tm_rgb() +
  tm_shape(shp = Human_Influence_Areas_Buffer) + 
    tm_polygons(col = "Human_Influence_Areas", palette = "grey") +
  tm_shape(shp = Wildschwein_select) +
    tm_dots(col = "TierName") +
    tm_compass(type = "arrow", position = c(0.247,0.028), bg.color = "white", bg.alpha = 0.75, size = 1.8) + 
  tm_scale_bar(breaks = c(0, 0.25, 0.5, 0.75 ,1), text.size = 0.75, position = c("left", "bottom"), bg.color = "white", bg.alpha = 0.75) +
  tm_layout(main.title = "b)",
            main.title.size = 1,
            legend.title.size = 1,
            legend.title.color = "white",
            legend.text.size = 0.6,
            legend.outside = TRUE,
            legend.bg.color = "white",
            legend.bg.alpha = 1)


### most fearful/brave wild boar

Wildboar_brave <- st_join(x = Wildschwein_select, y = Human_Influence_Areas_Buffer)

Wildboar_brave$objektart_Beschreibung <- replace_na(data = Wildboar_brave$objektart_Beschreibung, replace = "Wald&Wiesen&Natur")

Wildboar_brave <- mutate(Wildboar_brave, Human_Influence_Areas = if_else(objektart_Beschreibung == "Wald&Wiesen&Natur", "Nature", "Human_Influence_Areas"))

Wildboar_brave <- st_drop_geometry(Wildboar_brave)

Wildboar_brave <- mutate(Wildboar_brave,time_of_stay = as.integer(difftime(time1 = lead(DatetimeUTC), time2 = DatetimeUTC), units = "secs"))

Wildboar_brave <- Wildboar_brave[,c("TierName", "objektart_Beschreibung", "time_of_stay", "Human_Influence_Areas")]

Wildboar_brave_list <-  Wildboar_brave %>%
  filter(time_of_stay >0) %>%
  group_by(TierName, Human_Influence_Areas) %>%
  summarise(time_of_stay_s = sum(time_of_stay)) %>%
  mutate(time_of_stay_percent = (time_of_stay_s/timespan)*100)

Human_Influence_Areas_Plot

Human_Influence_Areas_Buffer_Plot

Figure 1: a) all human influence areas in the study area; b) the 100 m buffer zone around the human influence areas.

Where wild boars stay

To describe the preferred locations - such as forest, water bodies, wetlands and farmland - the wild boar data and the land cover data were first joined by st_join(). Then the time difference between subsequent rows was calculated with difftime(). The classification was based on the time spent in the preferred locations as a percentage over the entire study period (31 days). In Figure 2 are all land cover types in the study area shown.

Wildboar_Bodenbedeckung <- st_join(x = Wildschwein_select, y = TLM_Bodenbedeckung)

Wildboar_Bodenbedeckung <- st_drop_geometry(Wildboar_Bodenbedeckung)

Wildboar_Bodenbedeckung$objektart_Beschreibung <- replace_na(data = Wildboar_Bodenbedeckung$objektart_Beschreibung, replace = "Ackerland")

Wildboar_Bodenbedeckung <- mutate(Wildboar_Bodenbedeckung,time_of_stay = as.integer(difftime(time1 = lead(DatetimeUTC), time2 = DatetimeUTC), units = "secs"))

Wildboar_Bodenbedeckung <- Wildboar_Bodenbedeckung[,c("TierName", "objektart_Beschreibung", "time_of_stay")]

Wildboar_Bodenbedeckung <-  Wildboar_Bodenbedeckung %>%
  filter(time_of_stay >0) %>%
  group_by(TierName, objektart_Beschreibung) %>%
  summarise(time_of_stay_s = sum(time_of_stay)) %>%
  mutate(time_of_stay_percent = (time_of_stay_s/timespan)*100)


# Plot

Wildboar_LandCover_Plot <- tm_shape(shp = Orthophoto) + 
    tm_rgb() +
  tm_shape(shp = TLM_Bodenbedeckung) + 
    tm_polygons(col = "objektart_Beschreibung") + 
  tm_shape(shp = Wildschwein_select) + 
    tm_dots(col = "TierName") +
  tm_compass(type = "arrow", position = c(0.247,0.028), bg.color = "white", bg.alpha = 0.75, size = 1.8) +
  tm_scale_bar(breaks = c(0, 0.25, 0.5, 0.75 ,1), text.size = 0.75, position = c("left", "bottom"), bg.color = "white", bg.alpha = 0.75) + 
  tm_layout(main.title.size = 1,
            legend.title.size = 1,
            legend.title.color = "white",
            legend.text.size = 0.6,
            legend.outside = TRUE,
            legend.bg.color = "white",
            legend.bg.alpha = 1)
Wildboar_LandCover_Plot

Figure 2: Land cover types in the study area.

Results

The most active / laziest and most adventurous wild boar

First: Calculating Mean Steplengh , Second: Plotting with geom_tile , Y = TierName , X = mean hours of the day , fill = steplegth

Core result of the question answered: which is the most active / laziest wildboar and most adventurous wild boar. The interpretation is still a vage idea of what “active” / “laziest” means but in thsi project we defined it as the most active wildboar over a certain timeperiod. The results show clear “winners” and “loosers” when having a look on the mean_speed and mean_net_displacement over the evaluated timeperiod. We interpreted the results in the way that in our point of view the wildboar with the highest steplength, speed or netto displacement got evaluated as the most active wild boar and vice versa. In order to backup that we also derived the overall mean speed and netdisplacement of the individuals in order back up our hypothesis and therefore finding the most adventurous wild boar.

Different visualization approaches might be even more useful in order to derive the real overall “laziness” or “activeness”

# steplength_mean

ws_newyear_steplength_mean %>%
count(datetime_round, TierName) %>%
ggplot(mapping = aes(x = datetime_round, y = TierName)) +
geom_tile(mapping = aes(fill = ws_newyear_steplength_mean$steplength_mean)) 

# speed_mean

ws_newyear_speed_mean %>%
count(datetime_round, TierName) %>%
ggplot(mapping = aes(x = datetime_round, y = TierName)) +
geom_tile(mapping = aes(fill = ws_newyear_speed_mean$speed_mean)) 

View(ws_newyear_speed_mean)


#net displacement

ws_newyear_net_displacement %>%
count(datetime_round, TierName) %>%
ggplot(mapping = aes(x = datetime_round, y = TierName)) +
geom_tile(mapping = aes(fill = ws_newyear_net_displacement$net_displacement)) 

View(ws_newyear_net_displacement)

# i.e. the geom_point to derive the most active or laziest wildboar
ggplot(data = ws_newyear_steplength_mean, aes(datetime_round, steplength_mean, colour=TierName)) + geom_point()

# An alternative of the geom_tile visualization approach which basically does not show anything new.
# A more interesting visualization approach seems to be the use of the geom_boxplot:
  

ggplot(data = ws_newyear_speed_mean, aes(x=TierName, y = speed_mean)) +
geom_boxplot()

ggplot(data = ws_newyear_steplength_mean, aes(x=TierName, y = steplength_mean)) +
geom_boxplot()

ggplot(data = ws_newyear_net_displacement, aes(x=TierName, y = net_displacement)) +
geom_boxplot()

# These boxplots show the overall mean proxies of our indicator like speed /  steplength and net displacement of the individuals and are therefore are clearly the most adequate ways to visualize the overall activeness.The overall winner therefore seems to be the individual Fritz whereas he has the highest mean speed, steplength and net displacement over the hole timeperiod chosen.The overall most laziest wildboar therefore seems to be the wildboar indivual Sabine.


# This can also be expressed in numbers:

ws_newyear_speed %>%
  group_by(TierName) %>%
  subset(select=-c(E,N)) %>%
  summarise(mean = mean(speed, na.rm =TRUE))

## # A tibble: 7 × 2
##   TierName  mean
##   <chr>    <dbl>
## 1 Caroline 1.64 
## 2 Fritz    2.21 
## 3 Isabelle 1.63 
## 4 Nicole   1.47 
## 5 Rosa     1.61 
## 6 Ruth     0.576
## 7 Sabine   0.582

ws_newyear_steplength %>%
  group_by(TierName) %>%
  subset(select=-c(E,N)) %>%
  summarise(mean = mean(steplength, na.rm =TRUE))

## # A tibble: 7 × 2
##   TierName  mean
##   <chr>    <dbl>
## 1 Caroline 114. 
## 2 Fritz    160. 
## 3 Isabelle  74.1
## 4 Nicole    71.4
## 5 Rosa      80.4
## 6 Ruth      44.0
## 7 Sabine    21.0

# !
ws_newyear_net_displacement %>%
  group_by(TierName) %>%
  summarise(mean = mean(net_displacement, na.rm =TRUE))

## # A tibble: 7 × 2
##   TierName  mean
##   <chr>    <dbl>
## 1 Caroline  478.
## 2 Fritz     453.
## 3 Isabelle  259.
## 4 Nicole    359.
## 5 Rosa      333.
## 6 Ruth      413.
## 7 Sabine    152.

# Interestingly the last lines of code show a different result whereas according to the overall mean of net displacement the winner seems to be the individual Caroline. We therefore correct our crowning and give the titel of the most adventurous wild boar = therefore most traveled wild boar to the indivual Caroline.

The most fearful and the most brave wild boar

The effect of the human influence areas at the activity-patterns of the wild boars is shown in Figure 3. The duration of stay in urban areas is different for each animal. It is clearly seen, that the wild boars “Sabine” and “Ruth” venture closest to settlements. Whereas “Isabelle” and “Rosa” avoid the human influence areas and and prefer to stay in nature. The exact values and the ranking for each wild boar are given in table 2 & 3.

# Plot
Wildboar_brave_list_plot <- ggplot(Wildboar_brave_list, aes(x = TierName, y = time_of_stay_percent, fill = Human_Influence_Areas)) +
  geom_col() +
  theme_classic() +
  labs(y = "Duration of Stay [%]", x = "Wild Boar Name") +
  guides(fill=guide_legend(title="Land Type")) +
  scale_fill_manual(values = c("grey", "firebrick"))
Wildboar_brave_list_plot

Figure 3: Time stay in human influence areas and nature as a percentage over the entire study period (31 days) of the seven wild boars.

# Ranking brave
Wildboar_brave_rank <- Wildboar_brave_list %>%
  group_by(TierName) %>%
  filter(Human_Influence_Areas == "Human_Influence_Areas") %>%
  arrange(desc(time_of_stay_percent))
Wildboar_brave_rank$Rank <- 1:nrow(Wildboar_brave_rank)
Wildboar_brave_rank$Tier_Name <- Wildboar_brave_rank$TierName
Wildboar_brave_rank$Duration_of_Stay <- Wildboar_brave_rank$time_of_stay_percent
Wildboar_brave_rank <- Wildboar_brave_rank[,c("Tier_Name", "Duration_of_Stay", "Rank")]
Wildboar_brave_rank$Duration_of_Stay <- round(Wildboar_brave_rank$Duration_of_Stay, digits = 0)

kable(Wildboar_brave_rank, col.names = gsub("[_]", " ", names(Wildboar_brave_rank)), caption = "Table 2: The exact values and the ranking for the brave wild boar (Duration of Stay in Percent)")

Table 2: The exact values and the ranking for the brave wild boar (Duration of Stay in Percent)

Tier Name	Duration of Stay	Rank
Sabine	98	1
Ruth	97	2
Caroline	75	3
Fritz	53	4
Nicole	27	5
Rosa	14	6
Isabelle	13	7

# Ranking fearful
Wildboar_fearful_rank <- Wildboar_brave_list %>%
  group_by(TierName) %>%
  filter(Human_Influence_Areas == "Nature") %>%
  arrange(desc(time_of_stay_percent))
Wildboar_fearful_rank$Rank <- 1:nrow(Wildboar_fearful_rank)
Wildboar_fearful_rank$Tier_Name <- Wildboar_fearful_rank$TierName
Wildboar_fearful_rank$Duration_of_Stay <- Wildboar_fearful_rank$time_of_stay_percent
Wildboar_fearful_rank <- Wildboar_fearful_rank[,c("Tier_Name", "Duration_of_Stay", "Rank")]
Wildboar_fearful_rank$Duration_of_Stay <- round(Wildboar_fearful_rank$Duration_of_Stay, digits = 0)

kable(Wildboar_fearful_rank, col.names = gsub("[_]", " ", names(Wildboar_fearful_rank)), caption = "Table 2: The exact values and the ranking for the fearful wild boar (Duration of Stay in Percent)")

Table 2: The exact values and the ranking for the fearful wild boar (Duration of Stay in Percent)

Tier Name	Duration of Stay	Rank
Isabelle	87	1
Rosa	86	2
Nicole	73	3
Fritz	47	4
Caroline	25	5
Ruth	3	6
Sabine	2	7

Where wild boars stay

The preferred locations of wild boars are very different and individual. “Sabine” and “Ruth” for example are water-loving, staying mostly in water bodies and wetlands. Whereas “Caroline” and “Fritz” are more in woods and farmlands. Over all, wetlands are the most popular location for every wild boar. The time stay in the land cover types is shown in figure 4.

Wildboar_LandType_Plot <- ggplot(Wildboar_Bodenbedeckung, aes(x = TierName, y = time_of_stay_percent, fill = objektart_Beschreibung)) +
  geom_col() +
  theme_classic() +
  labs(y = "Duration of Stay [%]", x = "Wild Boar Name") +
  guides(fill=guide_legend(title="Land Type")) +
    scale_fill_brewer(palette = 14)
Wildboar_LandType_Plot

Figure 4: Time stay in the land cover types as a percentage over the entire study period (31 days) of the seven wild boars.

Discussion

Results and discussion of the research questions. Achievements and further steps.

In order to reduce the amount of data we chose 7 individuals over the time period of 15.12.2014 until the 15.01.2015. Our core results are the questions answered: Which is the most active / laziest and most adventurous wild boar. We therefore contextualized behavioral patterns given to a certain environment type (i.e. field, forest, settlement area) and answered the following questions: “What does a wild boar fear?” or “What are the behavioral constraints of wild boars?”.

The results showed clear “winners” and “losers” when having a look on the mean speed, mean step length and mean net displacement over the evaluated time period. We therefore interpreted the results in the way that the wild boar with the highest step length, speed got evaluated as the most active wild boar and vice versa. The wild boar with the highest net displacement got evaluated as the most adventurous.

The box plots show the overall mean proxies of our indicator like speed / step length and net displacement of the individuals. They seem to be the most adequate ways to visualize the overall activeness. The overall winner therefore seems to be the individual Fritz whereas he has the highest mean speed, step length and net displacement over the hole time period chosen. The overall laziest wild boar therefore seems to be the wild boar individual Sabine.

Interestingly whereas according to the overall mean of net displacement the winner seems to be the individual Caroline. We therefore added the title of the most adventurous wild boar equaling the most traveled wild boar to the individual Caroline.

The effect of the human influence areas at the activity-patterns of the wild boars is shown in Figure 2. The duration of close approximation to urban areas is different for each animal. The wild boars “Sabine” and “Ruth” venture closest to settlements. Whereas “Isabelle” and “Rosa” avoid close approximation to urban areas and prefer to stay in more remote areas like fields and forest. The exact values and the ranking for each wild boar are given in table 2 & 3.

Further steps for future research would be clearly to broaden the time period and therefore extending the number of individuals. The mean values could therefore be more informative because them bearing more overall information. Also, the contextualized movement patterns would expand the valuable number of psychological patterns because considering more overall data.

Report problems, limitations and solutions

The interpretation of what most “active” / “laziest” or “adventurous” means still remains a vague idea but in this project, we defined it as the fastest or most traveled wild boar over the given time period.

The Moving window approach in order to reduce noise of the data might have been another approach to analyze i.e. mean speed but was not further been elaborated in this project. The idea was to sum up – in this case six and five over the day mean variables like speed to smooth out movement peeks over the hole given time period of one month. This allows to visualize the movement patterns in a more trend driven way.

Bibliography

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